Application of basic Skills in Reading Arabic Text for Teaching and Learning Maharat Al-Qiraah

AbstractThe basic skill of reading Arabic text ought to be possessed by each at the Faculty of Islamic Studies (FIS) at UKM student in order to master other academic subjects. This study is the result of observing students’ weakness in the basic skill of reading Arabic text. The study sample consists of 45 Maharat al-Qiraah (Reading Skill) students. This study identifies this skill through observation and analysis of marks in the assesment test on student knowledge level of al-jarr and al-zarf (prepositions and adverbs) functions and level of basic skill in reading Arabic text. Research findings show that basic skill in reading Arabic text for FPI students is at moderate level. The result also show that there is a relationship between knowledge and skills levels, and that basic skill in reading Arabic text can be improved through workshops and continuous training

A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

Classification System for Wood Recognition using K-Nearest Neighbor with Optimized Features from Binary Gravitational Algorithm

Woods species recognition is a texture classification difficulty that has been studied by many researchers years ago. The species of the wood are identified by the proposed classification using the textural type that can be observed on the structural features for example the colour of the woods, weight, texture and other features. Any mistakes on texture recognition will affect the economic benefits of wood production where it is an important basis for an identification of woods. Besides, to guide a person to be skilled in wood recognition, it will take a long time and the result the wood sample can be biased. These kinds of problem can be a motivation to develop a system that can recognize the wood effectively. This project will try to integrate both attempts by proposing a classification system consists of feature extractor, classifier and optimizer. The project proposes a classification system using Gray Level Co-Occurrence Matrix (GLCM) as feature extractor, K-Nearest Neighbor (K-NN) as classifier and Binary Gravitational Search Algorithm (BGSA) as the optimizer for GLCM’s feature selection and parameters. For this project, images of wood knot from CAIRO UTM database are used for benchmarking the proposed system performance. The result shows that the proposed approach can perform as good as previous literatures with fewer features used as input for the classifier

PID offline tuning using gravitational search algorithm

PID controller is a common controller which is applied for many applications around the world. The simple structure and less effort of tuning make this controller being chosen in industrial and many research areas. The PID controller parameters usually tune heuristically to obtain the required output performance. This method has no systematic way of tuning procedure thus make the controller parameters tuning consuming a lot of time and effort. This problem will be more complicated when the system is dynamic and the performance of output response is the priority. With motivation of this problem, the Gravitational Search Algorithm (GSA) can be developed to make the process of PID controller tuning can be more easily and has systematic procedure of tuning method

Vulpia bromoides (L.) Gray (BR0000011622260)

Belgium Herbarium image of Meise Botanic Garden